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Revisiting Differentially Private Federated Learning for Tabular Data: A Matched-Accounting Benchmark of Boosting Versus DP-SGD
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DOI：10.3390/electronics15163597.png)
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Abstract
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Gradient-boosted trees outperform neural networks on tabular data without privacy, often taken to imply that differentially private federated learning should be based on boosting. We revisit this implication under matched accounting—a single privacy-loss distribution accountant, cross-checked against Rényi accounting—and symmetric, per-budget tuning, and find limited support for this expectation. On the Diabetes 130-US-Hospitals and BRFSS datasets across ε ∈ {0.5, 1, 2, 4, 8} over 20 seeds, differentially private federated boosting, a differentially private stochastic gradient descent (DP-SGD) network, and DP-SGD logistic regression achieve similar performance; no model class is consistently superior. On the real corpora, the most frugal model wins at the tightest budget—logistic regression is best at ε = 0.5 (0.589 and 0.811 AUC)—while the network leads slightly at looser budgets; boosting remains competitive but does not lead. On the synthetic task, boosting leads at tight budgets and the network at looser ones. The comparison is asymmetrically tuning-sensitive: fixing the boosting round count can produce an apparent neural advantage, whereas the network is robust to its step count. Off-path privatization and sequential noise accumulation explain the behavior; boosting’s main advantage is not accuracy but communication, achieving one to three orders of magnitude fewer values per client.
Keywords:
differential privacy
federated learning
gradient boosting
DP-SGD
tabular data
privacy accounting
benchmarking
healthcare AI
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